{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# figs_facebook.ipynb\n",
    "\n",
    "Author: MB\n",
    "\n",
    "Date run: June 16, 2021\n",
    "\n",
    "Location: HPC\n",
    "\n",
    "The purpose of this script is to read in a dataset of retweets of Trump tweets and generate three figures\n",
    "* the cumulative number of retweets over time\n",
    "* the average number of followers per retweeter\n",
    "* the average number of engagements per retweeter\n",
    "\n",
    "Data in: \n",
    "* Decahose (filtered to just retweets of Trump tweets) -- 10% of all tweets\n",
    "* Dataset of interventions to tweets\n",
    "* labels for Trump tweets\n",
    "* crowdtangle posts containing Trump tweets - Facebook\n",
    "\n",
    "Data out: the aforementioned figures"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# imports \n",
    "import datetime\n",
    "import glob\n",
    "import json\n",
    "import os\n",
    "import warnings\n",
    "warnings.filterwarnings(action='ignore')\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from tqdm import tqdm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def fill_frame(frame, tweet_id):\n",
    "    '''\n",
    "    this function forward fills metrics to fill in missing minutes where there is not a record of the number of retweets\n",
    "    it then returns the filled dataframe\n",
    "    \n",
    "    :pd.DataFrame frame: a dataframe of tweets with the number of seconds since posting `seconds` and number of retweets at that timestamp `count`\n",
    "    '''\n",
    "    # sort dataframe in ascending order by seconds\n",
    "    frame = frame.sort_values('seconds')\n",
    "    \n",
    "    # append the missing seconds to the dataframe with an empty count value\n",
    "    seconds_we_have = frame['seconds'].unique()\n",
    "    frame['count'] = range(len(frame)) \n",
    "    frame['count'] = frame['count'] + 1\n",
    "    frame = frame.append([{'seconds':i} for i in range(0, 86401) if i not in seconds_we_have]).sort_values('seconds')\n",
    "    \n",
    "    # forward fill the dataframe (carry values up to the next non-null value)\n",
    "    frame['count'] = frame['count'].fillna(method='ffill')\n",
    "    frame['tweet_id'] = tweet_id\n",
    "    \n",
    "    return frame\n",
    "\n",
    "def convert_datetime(time):\n",
    "    '''\n",
    "    converts twitter timestamp string into a datetime object\n",
    "    \n",
    "    :str time: the twitter-style timestamp for tweet creation\n",
    "    :returns date: a datetime.datetime object corresponding to the time input\n",
    "    '''\n",
    "    return datetime.datetime.strptime(time, '%a %b %d %H:%M:%S +0000 %Y')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# set colors\n",
    "no_intervention_color = '#16697a'\n",
    "soft_intervention_color = '#ffa62b'\n",
    "hard_intervention_color = '#721121'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# read in the intervention dataset\n",
    "interventions = pd.read_json('../data/interventions.json', orient='records', lines=True, dtype={'id_str':str})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# read in the labels and unique trump tweets \n",
    "labels = pd.read_csv('../data/labels.csv')\n",
    "trump_tweets = pd.read_csv('../data/trump_tweets.csv', dtype={'retweeted__id':str})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# copy `Final` data to `political` because I like that better\n",
    "trump_tweets['political'] = labels['Final']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# filter Trump tweets only to tweets labelled as political\n",
    "political_tweets = trump_tweets[trump_tweets['political'] == 'political']['retweeted__id'].tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# filter interventions to Trump tweets that are political\n",
    "interventions = interventions[interventions['id_str'].isin(political_tweets)].drop_duplicates('id_str').copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# get tweet IDs by intervention type\n",
    "no_intervention = interventions[(interventions['hard_intervention'] == False) & (interventions['soft_intervention'] == False)]['id_str'].tolist()\n",
    "soft_intervention = interventions[(interventions['hard_intervention'] == False) & (interventions['soft_intervention'] == True)]['id_str'].tolist()\n",
    "hard_intervention = interventions[(interventions['hard_intervention'] == True)]['id_str'].tolist()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Facebook Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# read in crowdtangle posts\n",
    "crowdtangle_posts = pd.read_json('../data/crowdtangle_posts-facebook.json', orient='records', lines=True,\n",
    "                                 dtype={'tweet_id':str})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# cast crowdtangle date to utc\n",
    "crowdtangle_posts['date'] = crowdtangle_posts['date'].dt.tz_localize('UTC')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# filter crowdtangle posts to those that we have intervention types for\n",
    "crowdtangle_posts = crowdtangle_posts[crowdtangle_posts['tweet_id'].isin(interventions['id_str'])].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# merge crowdtangle posts with the timestamp data from the tweet (on the tweet ID that matches them)\n",
    "fb_timestamps = pd.merge(crowdtangle_posts[['tweet_id', 'date']], interventions[['created_at', 'id_str']], \n",
    "                         left_on='tweet_id', right_on='id_str')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# filter facebook posts to those that happened within 24 hours of the tweet publish date\n",
    "fb_timestamps = fb_timestamps[(fb_timestamps['date'] - fb_timestamps['created_at'] > datetime.timedelta(minutes=0))\n",
    "                              & (fb_timestamps['date'] - fb_timestamps['created_at'] <= datetime.timedelta(hours=24))\n",
    "                             ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# calcuate the difference in time between the tweet publish date and the facebook post\n",
    "fb_timestamps['time_diff'] = (fb_timestamps['date'] - fb_timestamps['created_at']).values / np.timedelta64(1, 's')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create minutes and hours variables\n",
    "fb_timestamps['seconds'] = fb_timestamps['time_diff']\n",
    "fb_timestamps['minutes'] = fb_timestamps['seconds'] / 60\n",
    "fb_timestamps['hours'] = fb_timestamps['minutes'] / 60"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# fill each of the tweet frames to fill missing minutes\n",
    "filled_frames = pd.concat([fill_frame(frame, _) for _, frame in fb_timestamps.groupby('tweet_id')])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# split filled frames by intervention type\n",
    "hard = filled_frames[filled_frames['tweet_id'].isin(hard_intervention)].copy()\n",
    "soft = filled_frames[filled_frames['tweet_id'].isin(soft_intervention)].copy()\n",
    "none = filled_frames[filled_frames['tweet_id'].isin(no_intervention)].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# take 1 hour rolling average of tweet counts\n",
    "hard_plot = hard.groupby('seconds')['count'].mean().rolling(window=3600, min_periods=20).mean()\n",
    "soft_plot = soft.groupby('seconds')['count'].mean().rolling(window=3600, min_periods=20).mean()\n",
    "none_plot = none.groupby('seconds')['count'].mean().rolling(window=3600, min_periods=20).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# create growth figure\n",
    "plt.figure(figsize=(15,10))\n",
    "plt.plot(hard_plot, label='Hard Intervention', color=hard_intervention_color, linewidth=4)\n",
    "plt.plot(soft_plot, label='Soft Intervention', color=soft_intervention_color, linewidth=4)\n",
    "plt.plot(none_plot, label='No Intervention', color=no_intervention_color, linewidth=4)\n",
    "\n",
    "plt.legend(fontsize=14)\n",
    "locs = [i*3600 for i in range(25)]\n",
    "plt.xticks(locs, range(25), rotation=45, fontsize=14)\n",
    "plt.yticks(fontsize=14)\n",
    "plt.legend(fontsize=14)\n",
    "plt.xlabel('Time since tweet publication (hours)', fontsize=14)\n",
    "plt.ylabel('Number of Facebook Posts', fontsize=14)\n",
    "plt.title('Growth of Facebook Posts by Intervention', fontsize=14)\n",
    "plt.gca().spines['top'].set_visible(False)\n",
    "plt.gca().spines['right'].set_visible(False)\n",
    "plt.savefig('../plots/facebook_spread_of_tweets.png', dpi=600)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# filter crowdtangle posts by intervention type\n",
    "hard_ct = crowdtangle_posts[crowdtangle_posts['tweet_id'].isin(hard_intervention)].copy()\n",
    "soft_ct = crowdtangle_posts[crowdtangle_posts['tweet_id'].isin(soft_intervention)].copy()\n",
    "none_ct = crowdtangle_posts[crowdtangle_posts['tweet_id'].isin(no_intervention)].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# create average page subscriber figure\n",
    "plt.figure(figsize=(8, 10))\n",
    "\n",
    "plt.bar(0, width=.5, height=hard_ct.groupby('tweet_id')['subscriberCount'].sum().mean(), color=hard_intervention_color, label='Hard Intervention')\n",
    "plt.bar(1, width=.5, height=soft_ct.groupby('tweet_id')['subscriberCount'].sum().mean(), color=soft_intervention_color, label='Soft Itervention')\n",
    "plt.bar(2, width=.5, height=none_ct.groupby('tweet_id')['subscriberCount'].sum().mean(), color=no_intervention_color, label='No Intervention')\n",
    "plt.legend(fontsize=14)\n",
    "plt.xticks([])\n",
    "plt.yticks(fontsize=14)\n",
    "plt.legend(fontsize=14, bbox_to_anchor = [1.5, .5])\n",
    "plt.xlabel('Intervention Type', fontsize=14)\n",
    "plt.ylabel('Average Total Page Subscribers', fontsize=14)\n",
    "plt.title('Average Total Page Subscribers by Intervention Type (Facebook)', fontsize=14)\n",
    "plt.gca().spines['top'].set_visible(False)\n",
    "plt.gca().spines['right'].set_visible(False)\n",
    "plt.savefig('../plots/facebook_avg_exposure.png', dpi=600)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# create average interaction figure\n",
    "plt.figure(figsize=(8, 10))\n",
    "\n",
    "plt.bar(0, width=.5, height=hard_ct.groupby('tweet_id')['total_statistics'].sum().mean(), color=hard_intervention_color, label='Hard Intervention')\n",
    "plt.bar(1, width=.5, height=soft_ct.groupby('tweet_id')['total_statistics'].sum().mean(), color=soft_intervention_color, label='Soft Itervention')\n",
    "plt.bar(2, width=.5, height=none_ct.groupby('tweet_id')['total_statistics'].sum().mean(), color=no_intervention_color, label='No Intervention')\n",
    "plt.legend(fontsize=14)\n",
    "plt.xticks([])\n",
    "plt.yticks(fontsize=14)\n",
    "plt.legend(fontsize=14, bbox_to_anchor = [1.5, .5])\n",
    "plt.xlabel('Intervention Type', fontsize=14)\n",
    "plt.ylabel('Average Total Interactions', fontsize=14)\n",
    "plt.title('Average Total Interactions by Intervention Type (Facebook)', fontsize=14)\n",
    "plt.gca().spines['top'].set_visible(False)\n",
    "plt.gca().spines['right'].set_visible(False)\n",
    "plt.savefig('../plots/facebook_avg_engagements.png', dpi=600)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
